AutoML Leaderboard
AutoML Performance

AutoML Performance Boxplot

Features Importance

Spearman Correlation of Models

Summary of 5_Default_NeuralNetwork
<< Go back
Neural Network
- n_jobs: -1
- dense_1_size: 32
- dense_2_size: 16
- learning_rate: 0.05
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
2.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.808877 |
nan |
| auc |
0.541889 |
nan |
| f1 |
0.660714 |
0.000300195 |
| accuracy |
0.540667 |
0.495761 |
| precision |
0.603448 |
0.850206 |
| recall |
1 |
0.000300195 |
| mcc |
0.0848235 |
0.495761 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.808877 |
nan |
| auc |
0.541889 |
nan |
| f1 |
0.573902 |
0.495761 |
| accuracy |
0.540667 |
0.495761 |
| precision |
0.529076 |
0.495761 |
| recall |
0.627027 |
0.495761 |
| mcc |
0.0848235 |
0.495761 |
Confusion matrix (at threshold=0.495761)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
347 |
413 |
| Labeled as 1 |
276 |
464 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 1_Baseline
<< Go back
Baseline Classifier (Baseline)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
0.3 seconds
Metric details
|
score |
threshold |
| logloss |
0.693059 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.660714 |
0.4436 |
| accuracy |
0.493333 |
0.4436 |
| precision |
0.493333 |
0.4436 |
| recall |
1 |
0.4436 |
| mcc |
0 |
0.4436 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.693059 |
nan |
| auc |
0.5 |
nan |
| f1 |
0.660714 |
0.4436 |
| accuracy |
0.493333 |
0.4436 |
| precision |
0.493333 |
0.4436 |
| recall |
1 |
0.4436 |
| mcc |
0 |
0.4436 |
Confusion matrix (at threshold=0.4436)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
0 |
760 |
| Labeled as 1 |
0 |
740 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of Ensemble
<< Go back
Ensemble structure
| Model |
Weight |
| 3_Linear |
2 |
| 4_Default_Xgboost |
1 |
Metric details
|
score |
threshold |
| logloss |
0.682195 |
nan |
| auc |
0.59864 |
nan |
| f1 |
0.661239 |
0.239071 |
| accuracy |
0.582667 |
0.499564 |
| precision |
0.763158 |
0.733148 |
| recall |
1 |
0.106754 |
| mcc |
0.165128 |
0.499564 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.682195 |
nan |
| auc |
0.59864 |
nan |
| f1 |
0.575881 |
0.499564 |
| accuracy |
0.582667 |
0.499564 |
| precision |
0.577446 |
0.499564 |
| recall |
0.574324 |
0.499564 |
| mcc |
0.165128 |
0.499564 |
Confusion matrix (at threshold=0.499564)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
449 |
311 |
| Labeled as 1 |
315 |
425 |
Learning curves

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

<< Go back
Summary of 2_DecisionTree
<< Go back
Decision Tree
- n_jobs: -1
- criterion: gini
- max_depth: 3
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
6.4 seconds
Metric details
|
score |
threshold |
| logloss |
0.698446 |
nan |
| auc |
0.510386 |
nan |
| f1 |
0.660714 |
0.228814 |
| accuracy |
0.513333 |
0.551724 |
| precision |
0.590909 |
0.665424 |
| recall |
1 |
0.228814 |
| mcc |
0.0307276 |
0.551724 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.698446 |
nan |
| auc |
0.510386 |
nan |
| f1 |
0.126794 |
0.551724 |
| accuracy |
0.513333 |
0.551724 |
| precision |
0.552083 |
0.551724 |
| recall |
0.0716216 |
0.551724 |
| mcc |
0.0307276 |
0.551724 |
Confusion matrix (at threshold=0.551724)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
717 |
43 |
| Labeled as 1 |
687 |
53 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 6_Default_RandomForest
<< Go back
Random Forest
- n_jobs: -1
- criterion: gini
- max_features: 0.9
- min_samples_split: 30
- max_depth: 4
- eval_metric_name: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
10.5 seconds
Metric details
|
score |
threshold |
| logloss |
0.687506 |
nan |
| auc |
0.567825 |
nan |
| f1 |
0.663307 |
0.443609 |
| accuracy |
0.552 |
0.489128 |
| precision |
0.635417 |
0.53655 |
| recall |
1 |
0.362554 |
| mcc |
0.105715 |
0.489128 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.687506 |
nan |
| auc |
0.567825 |
nan |
| f1 |
0.569231 |
0.489128 |
| accuracy |
0.552 |
0.489128 |
| precision |
0.541463 |
0.489128 |
| recall |
0.6 |
0.489128 |
| mcc |
0.105715 |
0.489128 |
Confusion matrix (at threshold=0.489128)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
384 |
376 |
| Labeled as 1 |
296 |
444 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 4_Default_Xgboost
<< Go back
Extreme Gradient Boosting (Xgboost)
- n_jobs: -1
- objective: binary:logistic
- eta: 0.075
- max_depth: 6
- min_child_weight: 1
- subsample: 1.0
- colsample_bytree: 1.0
- eval_metric: auc
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
33.0 seconds
Metric details
|
score |
threshold |
| logloss |
0.691952 |
nan |
| auc |
0.583604 |
nan |
| f1 |
0.660714 |
0.0736868 |
| accuracy |
0.577333 |
0.510962 |
| precision |
0.65 |
0.764196 |
| recall |
1 |
0.0736868 |
| mcc |
0.154109 |
0.510962 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.691952 |
nan |
| auc |
0.583604 |
nan |
| f1 |
0.553521 |
0.510962 |
| accuracy |
0.577333 |
0.510962 |
| precision |
0.577941 |
0.510962 |
| recall |
0.531081 |
0.510962 |
| mcc |
0.154109 |
0.510962 |
Confusion matrix (at threshold=0.510962)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
473 |
287 |
| Labeled as 1 |
347 |
393 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back
Summary of 3_Linear
<< Go back
Logistic Regression (Linear)
- n_jobs: -1
- explain_level: 2
Validation
- validation_type: split
- train_ratio: 0.75
- shuffle: True
- stratify: True
Optimized metric
auc
Training time
3.5 seconds
Metric details
|
score |
threshold |
| logloss |
0.686974 |
nan |
| auc |
0.595857 |
nan |
| f1 |
0.660714 |
0.0801389 |
| accuracy |
0.576 |
0.492873 |
| precision |
0.675 |
0.717623 |
| recall |
1 |
0.0801389 |
| mcc |
0.152125 |
0.492873 |
Metric details with threshold from accuracy metric
|
score |
threshold |
| logloss |
0.686974 |
nan |
| auc |
0.595857 |
nan |
| f1 |
0.574866 |
0.492873 |
| accuracy |
0.576 |
0.492873 |
| precision |
0.568783 |
0.492873 |
| recall |
0.581081 |
0.492873 |
| mcc |
0.152125 |
0.492873 |
Confusion matrix (at threshold=0.492873)
|
Predicted as 0 |
Predicted as 1 |
| Labeled as 0 |
434 |
326 |
| Labeled as 1 |
310 |
430 |
Learning curves

Permutation-based Importance

Confusion Matrix

Normalized Confusion Matrix

ROC Curve

Kolmogorov-Smirnov Statistic

Precision-Recall Curve

Calibration Curve

Cumulative Gains Curve

Lift Curve

SHAP Importance

<< Go back